Political Donations of Professional Sports Team Owners

Owners Who Donate The Most Frequently and How Much

Top 25 Highest Political Donations by NFL Team Owners in 2016

Blockbusters

## # A tibble: 430 × 13
##    release_year rank_in_year imdb_rating mpaa_rating film_title      film_budget
##           <dbl>        <dbl>       <dbl> <chr>       <chr>                 <dbl>
##  1         2019            1         8.5 PG-13       Avengers: Endg…   356000000
##  2         2019            2         7   PG          The Lion King     260000000
##  3         2019            3         7.2 PG          Frozen II         150000000
##  4         2019            4         7.6 PG-13       Spider-Man: Fa…   160000000
##  5         2019            5         6.9 PG-13       Captain Marvel    175000000
##  6         2019            6         7.9 G           Toy Story 4       200000000
##  7         2019            7         8.6 R           Joker              55000000
##  8         2019            8         7   PG          Aladdin           183000000
##  9         2019            9         6.9 PG-13       Star Wars: Epi…   200000000
## 10         2019           10         6.5 PG-13       Fast & Furious…   200000000
## # … with 420 more rows, and 7 more variables: length_in_min <dbl>,
## #   domestic_distributor <chr>, worldwide_gross <dbl>, domestic_gross <dbl>,
## #   genre_1 <chr>, genre_2 <chr>, genre_3 <chr>

5 Highest & Lowest IMBD Ratings Highlighted by Title

## # A tibble: 5 × 2
##   mpaa_rating     n
##   <chr>       <int>
## 1 G              23
## 2 PG            133
## 3 PG-13         178
## 4 R              95
## 5 <NA>            1

History of Rock

## # A tibble: 5,484 × 18
##    index name           artist   release_date length popularity danceability...7
##    <dbl> <chr>          <chr>           <dbl>  <dbl>      <dbl>            <dbl>
##  1     0 Smells Like T… Nirvana          1991   5.03         74            0.502
##  2     1 Stairway to H… Led Zep…         1971   8.05         78            0.338
##  3     2 Bohemian Rhap… Queen            1975   5.91         74            0.392
##  4     3 Imagine - Rem… John Le…         1971   3.13         77            0.547
##  5     4 (I Can't Get … The Rol…         1965   3.71         77            0.723
##  6     5 Hotel Califor… Eagles           1976   6.52         83            0.579
##  7     6 Enter Sandman  Metalli…         1991   5.53         74            0.579
##  8     7 Whole Lotta L… Led Zep…         1969   5.56         77            0.412
##  9     8 Comfortably N… Pink Fl…         1979   6.37         74            0.472
## 10     9 One            U2               1991   4.60         76            0.392
## # … with 5,474 more rows, and 11 more variables: acousticness <dbl>,
## #   danceability...9 <dbl>, energy <dbl>, instrumentalness <dbl>, key <dbl>,
## #   liveness <dbl>, loudness <dbl>, speechiness <dbl>, tempo <dbl>,
## #   time_signature <dbl>, valence <dbl>

Unemployment

unemployment_data_us <- read_csv("data/unemployment_data_us.csv")
## Rows: 132 Columns: 13
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (2): Month, Date
## dbl (11): Year, Primary_School, High_School, Associates_Degree, Professional...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
unemployment_data_us
## # A tibble: 132 × 13
##     Year Month Primary_School Date     High_School Associates_Degree
##    <dbl> <chr>          <dbl> <chr>          <dbl>             <dbl>
##  1  2010 Jan             15.3 Jan-2010        10.2               8.6
##  2  2011 Jan             14.3 Jan-2011         9.5               8.1
##  3  2012 Jan             13   Jan-2012         8.5               7.1
##  4  2013 Jan             12   Jan-2013         8.1               6.9
##  5  2014 Jan              9.4 Jan-2014         6.5               5.9
##  6  2015 Jan              8.3 Jan-2015         5.4               5.2
##  7  2016 Jan              7.1 Jan-2016         5.2               4.2
##  8  2017 Jan              7.4 Jan-2017         5.2               3.8
##  9  2018 Jan              5.4 Jan-2018         4.4               3.4
## 10  2019 Jan              5.7 Jan-2019         3.7               3.4
## # … with 122 more rows, and 7 more variables: Professional_Degree <dbl>,
## #   White <dbl>, Black <dbl>, Asian <dbl>, Hispanic <dbl>, Men <dbl>,
## #   Women <dbl>